With the rapid growth of deep learning, handcrafted features and predefined filter banks in the BoW paradigm were replaced by the `deep features' and have achieved state of-the-art results [1]}, [2]}. The main challenge faced by these methods is that each component is optimized in a separate step, and features and encoders are fixed once built. Therefore, feature learning (CNNs and dictionary) does not benefit from labelled data. Recently, a deep dictionary learning approach has been proposed for texture recognition [3]}. It outperforms existing modular methods and achieves the state-of-the-art results on material/texture datasets. Also, it is an end-to-end learning framework whereby features, dictionaries, encoding representation and the classifier are all learned simultaneously facilitated by a suitable loss function.
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